Integrated requirement of non-specific and sequence-specific DNA binding in Myc-driven transcription.

Eukaryotic transcription factors recognize specific DNA sequence motifs, but are also endowed with generic, non-specific DNA-binding activity. How these binding modes are integrated to determine select transcriptional outputs remains unresolved. We addressed this question by site-directed mutagenesis of the Myc transcription factor. Impairment of non-specific DNA backbone contacts caused pervasive loss of genome interactions and gene regulation, associated with increased intra-nuclear mobility of the Myc protein in murine cells. In contrast, a mutant lacking base-specific contacts retained DNA-binding and mobility profiles comparable to those of the wild-type protein, but failed to recognize its consensus binding motif (E-box) and could not activate Myc-target genes. Incidentally, this mutant gained weak affinity for an alternative motif, driving aberrant activation of different genes. Altogether, our data show that non-specific DNA binding is required to engage onto genomic regulatory regions; sequence recognition in turn contributes to transcriptional activation, acting at distinct levels: stabilization and positioning of Myc onto DNA, and-unexpectedly-promotion of its transcriptional activity. Hence, seemingly pervasive genome interaction profiles, as detected by ChIP-seq, actually encompass diverse DNA-binding modalities, driving defined, sequence-dependent transcriptional responses.

PML modulates epigenetic composition of chromatin to regulate expression of pro-metastatic genes in triple-negative breast cancer.

The promyelocytic leukemia (PML) protein organizes nuclear aggregates known as PML nuclear bodies (PML-NBs), where many transcription factors localize to be regulated. In addition, associations of PML and PML-NBs with chromatin are described in various cell types, further implicating PML in transcriptional regulation. However, a complete understanding of the functional consequences of PML association to DNA in cellular contexts where it promotes relevant phenotypes is still lacking. We examined PML chromatin association in triple-negative breast cancer (TNBC) cell lines, where it exerts important oncogenic functions. We find that PML associates discontinuously with large heterochromatic PML-associated domains (PADs) that contain discrete gene-rich euchromatic sub-domains locally depleted of PML. PML promotes heterochromatic organization in PADs and expression of pro-metastatic genes embedded in these sub-domains. Importantly, this occurs outside PML-NBs, suggesting that nucleoplasmic PML exerts a relevant gene regulatory function. We also find that PML plays indirect regulatory roles in TNBC cells by promoting the expression of pro-metastatic genes outside PADs. Our findings suggest that PML is an important transcriptional regulator of pro-oncogenic metagenes in TNBC cells, via transcriptional regulation and epigenetic organization of heterochromatin domains that embed regions of local transcriptional activity.

Joubert syndrome-derived induced pluripotent stem cells show altered neuronal differentiation in vitro.

Joubert syndrome (JS) is a recessively inherited congenital ataxia characterized by hypotonia, psychomotor delay, abnormal ocular movements, intellectual disability, and a peculiar cerebellar and brainstem malformation, the "molar tooth sign." Over 40 causative genes have been reported, all encoding for proteins implicated in the structure or functioning of the primary cilium, a subcellular organelle widely present in embryonic and adult tissues. In this paper, we developed an in vitro neuronal differentiation model using patient-derived induced pluripotent stem cells (iPSCs), to evaluate possible neurodevelopmental defects in JS. To this end, iPSCs from four JS patients harboring mutations in distinct JS genes (AHI1, CPLANE1, TMEM67, and CC2D2A) were differentiated alongside healthy control cells to obtain mid-hindbrain precursors and cerebellar granule cells. Differentiation was monitored over 31 days through the detection of lineage-specific marker expression by qRT-PCR, immunofluorescence, and transcriptomics analysis. All JS patient-derived iPSCs, regardless of the mutant gene, showed a similar impairment to differentiate into mid-hindbrain and cerebellar granule cells when compared to healthy controls. In addition, analysis of primary cilium count and morphology showed notable ciliary defects in all differentiating JS patient-derived iPSCs compared to controls. These results confirm that patient-derived iPSCs are an accessible and relevant in vitro model to analyze cellular phenotypes connected to the presence of JS gene mutations in a neuronal context.

Accurate and highly interpretable prediction of gene expression from histone modifications.

BACKGROUND: Histone Mark Modifications (HMs) are crucial actors in gene regulation, as they actively remodel chromatin to modulate transcriptional activity: aberrant combinatorial patterns of HMs have been connected with several diseases, including cancer. HMs are, however, reversible modifications: understanding their role in disease would allow the design of 'epigenetic drugs' for specific, non-invasive treatments. Standard statistical techniques were not entirely successful in extracting representative features from raw HM signals over gene locations. On the other hand, deep learning approaches allow for effective automatic feature extraction, but at the expense of model interpretation. RESULTS: Here, we propose ShallowChrome, a novel computational pipeline to model transcriptional regulation via HMs in both an accurate and interpretable way. We attain state-of-the-art results on the binary classification of gene transcriptional states over 56 cell-types from the REMC database, largely outperforming recent deep learning approaches. We interpret our models by extracting insightful gene-specific regulative patterns, and we analyse them for the specific case of the PAX5 gene over three differentiated blood cell lines. Finally, we compare the patterns we obtained with the characteristic emission patterns of ChromHMM, and show that ShallowChrome is able to coherently rank groups of chromatin states w.r.t. their transcriptional activity. CONCLUSIONS: In this work we demonstrate that it is possible to model HM-modulated gene expression regulation in a highly accurate, yet interpretable way. Our feature extraction algorithm leverages on data downstream the identification of enriched regions to retrieve gene-wise, statistically significant and dynamically located features for each HM. These features are highly predictive of gene transcriptional state, and allow for accurate modeling by computationally efficient logistic regression models. These models allow a direct inspection and a rigorous interpretation, helping to formulate quantifiable hypotheses.

An early Myc-dependent transcriptional program orchestrates cell growth during B-cell activation.

Upon activation, lymphocytes exit quiescence and undergo substantial increases in cell size, accompanied by activation of energy-producing and anabolic pathways, widespread chromatin decompaction, and elevated transcriptional activity. These changes depend upon prior induction of the Myc transcription factor, but how Myc controls them remains unclear. We addressed this issue by profiling the response to LPS stimulation in wild-type and c-myc-deleted primary mouse B-cells. Myc is rapidly induced, becomes detectable on virtually all active promoters and enhancers, but has no direct impact on global transcriptional activity. Instead, Myc contributes to the swift up- and down-regulation of several hundred genes, including many known regulators of the aforementioned cellular processes. Myc-activated promoters are enriched for E-box consensus motifs, bind Myc at the highest levels, and show enhanced RNA Polymerase II recruitment, the opposite being true at down-regulated loci. Remarkably, the Myc-dependent signature identified in activated B-cells is also enriched in Myc-driven B-cell lymphomas: hence, besides modulation of new cancer-specific programs, the oncogenic action of Myc may largely rely on sustained deregulation of its normal physiological targets.

Chest CT in the emergency department for suspected COVID-19 pneumonia.

PURPOSE: In overwhelmed emergency departments (EDs) facing COVID-19 outbreak, a swift diagnosis is imperative. CT role was widely debated for its limited specificity. Here we report the diagnostic role of CT in two EDs in Lombardy, epicenter of Italian outbreak. MATERIAL AND METHODS: Admitting chest CT from 142 consecutive patients with suspected COVID-19 were retrospectively analyzed. CT scans were classified in "highly likely," "likely," and "unlikely" COVID-19 pneumonia according to the presence of typical, indeterminate, and atypical findings, or "negative" in the absence of findings, or "alternative diagnosis" when a different diagnosis was found. Nasopharyngeal swab results, turnaround time, and time to positive results were collected. CT diagnostic performances were assessed considering RT-PCR as reference standard. RESULTS: Most of cases (96/142, 68%) were classified as "highly likely" COVID-19 pneumonia. Ten (7%) and seven (5%) patients were classified as "likely" and "unlikely" COVID-19 pneumonia, respectively. In 21 (15%) patients a differential diagnosis was provided, including typical pneumonia, pulmonary edema, neoplasia, and pulmonary embolism. CT was negative in 8/142 (6%) patients. Mean turnaround time for the first COVID-19 RT-PCR was 30 ± 13 h. CT diagnostic accuracy in respect of the first test swab was 79% and increased to 91.5% after repeated swabs and/or BAL, for 18 false-negative first swab. CT performance was good with 76% specificity, 99% sensitivity, 90% positive predictive value and 97% negative predictive value. CONCLUSION: Chest CT was useful to streamline patients' triage while waiting for RT-PCR in the ED, supporting the clinical suspicion of COVID-19 or providing alternative diagnosis.

Integrative analysis of RNA polymerase II and transcriptional dynamics upon MYC activation.

Overexpression of the MYC transcription factor causes its widespread interaction with regulatory elements in the genome but leads to the up- and down-regulation of discrete sets of genes. The molecular determinants of these selective transcriptional responses remain elusive. Here, we present an integrated time-course analysis of transcription and mRNA dynamics following MYC activation in proliferating mouse fibroblasts, based on chromatin immunoprecipitation, metabolic labeling of newly synthesized RNA, extensive sequencing, and mathematical modeling. Transcriptional activation correlated with the highest increases in MYC binding at promoters. Repression followed a reciprocal scenario, with the lowest gains in MYC binding. Altogether, the relative abundance (henceforth, "share") of MYC at promoters was the strongest predictor of transcriptional responses in diverse cell types, predominating over MYC's association with the corepressor ZBTB17 (also known as MIZ1). MYC activation elicited immediate loading of RNA polymerase II (RNAPII) at activated promoters, followed by increases in pause-release, while repressed promoters showed opposite effects. Gains and losses in RNAPII loading were proportional to the changes in the MYC share, suggesting that repression by MYC may be partly indirect, owing to competition for limiting amounts of RNAPII. Secondary to the changes in RNAPII loading, the dynamics of elongation and pre-mRNA processing were also rapidly altered at MYC regulated genes, leading to the transient accumulation of partially or aberrantly processed mRNAs. Altogether, our results shed light on how overexpressed MYC alters the various phases of the RNAPII cycle and the resulting transcriptional response.

Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis.

The c-myc proto-oncogene product, Myc, is a transcription factor that binds thousands of genomic loci. Recent work suggested that rather than up- and downregulating selected groups of genes, Myc targets all active promoters and enhancers in the genome (a phenomenon termed 'invasion') and acts as a general amplifier of transcription. However, the available data did not readily discriminate between direct and indirect effects of Myc on RNA biogenesis. We addressed this issue with genome-wide chromatin immunoprecipitation and RNA expression profiles during B-cell lymphomagenesis in mice, in cultured B cells and fibroblasts. Consistent with long-standing observations, we detected general increases in total RNA or messenger RNA copies per cell (hereby termed 'amplification') when comparing actively proliferating cells with control quiescent cells: this was true whether cells were stimulated by mitogens (requiring endogenous Myc for a proliferative response) or by deregulated, oncogenic Myc activity. RNA amplification and promoter/enhancer invasion by Myc were separable phenomena that could occur without one another. Moreover, whether or not associated with RNA amplification, Myc drove the differential expression of distinct subsets of target genes. Hence, although having the potential to interact with all active or poised regulatory elements in the genome, Myc does not directly act as a global transcriptional amplifier. Instead, our results indicate that Myc activates and represses transcription of discrete gene sets, leading to changes in cellular state that can in turn feed back on global RNA production and turnover.

MuSERA: Multiple Sample Enriched Region Assessment.

Enriched region (ER) identification is a fundamental step in several next-generation sequencing (NGS) experiment types. Yet, although NGS experimental protocols recommend producing replicate samples for each evaluated condition and their consistency is usually assessed, typically pipelines for ER identification do not consider available NGS replicates. This may alter genome-wide descriptions of ERs, hinder significance of subsequent analyses on detected ERs and eventually preclude biological discoveries that evidence in replicate could support. MuSERA is a broadly useful stand-alone tool for both interactive and batch analysis of combined evidence from ERs in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser. We review MuSERA rationale and implementation, and illustrate how sets of significant ERs are expanded by applying MuSERA on replicates for several types of NGS data, including ChIP-seq of transcription factors or histone marks and DNase-seq hypersensitive sites. We show that MuSERA can determine a new, enhanced set of ERs for each sample by locally combining evidence on replicates, and prove how the easy-to-use interactive graphical displays and quantitative evaluations that MuSERA provides effectively support thorough inspection of obtained results and evaluation of their biological content, facilitating their understanding and biological interpretations. MuSERA is freely available at http://www.bioinformatics.deib.polimi.it/MuSERA/.

FunChIP: an R/Bioconductor package for functional classification of ChIP-seq shapes.

SUMMARY: Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) generates local accumulations of sequencing reads on the genome ("peaks"), which correspond to specific protein-DNA interactions or chromatin modifications. Peaks are detected by considering their total area above a background signal, usually neglecting their shapes, which instead may convey additional biological information. We present FunChIP, an R/Bioconductor package for clustering peaks according to a functional representation of their shapes: after approximating their profiles with cubic B-splines, FunChIP minimizes their functional distance and classifies the peaks applying a k-mean alignment and clustering algorithm. The whole pipeline is user-friendly and provides visualization functions for a quick inspection of the results. An application to the transcription factor Myc in 3T9 murine fibroblasts shows that clusters of peaks with different shapes are associated with different genomic locations and different transcriptional regulatory activity. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and is available under Artistic Licence 2.0 from the Bioconductor website (http://bioconductor.org/packages/FunChIP). CONTACT: marco.morelli@iit.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mutual epithelium-macrophage dependency in liver carcinogenesis mediated by ST18.

The ST18 gene has been proposed to act either as a tumor suppressor or as an oncogene in different human cancers, but direct evidence for its role in tumorigenesis has been lacking thus far. Here, we demonstrate that ST18 is critical for tumor progression and maintenance in a mouse model of liver cancer, based on oncogenic transformation and adoptive transfer of primary precursor cells (hepatoblasts). ST18 messenger RNA (mRNA) and protein were detectable neither in normal liver nor in cultured hepatoblasts, but were readily expressed after subcutaneous engraftment and tumor growth. ST18 expression in liver cells was induced by inflammatory cues, including acute or chronic inflammation in vivo, as well as coculture with macrophages in vitro. Knocking down the ST18 mRNA in transplanted hepatoblasts delayed tumor progression. Induction of ST18 knockdown in pre-established tumors caused rapid tumor involution associated with pervasive morphological changes, proliferative arrest, and apoptosis in tumor cells, as well as depletion of tumor-associated macrophages, vascular ectasia, and hemorrhage. Reciprocally, systemic depletion of macrophages in recipient animals had very similar phenotypic consequences, impairing either tumor development or maintenance, and suppressing ST18 expression in hepatoblasts. Finally, RNA sequencing of ST18-depleted tumors before involution revealed down-regulation of inflammatory response genes, pointing to the suppression of nuclear factor kappa B-dependent transcription. CONCLUSION: ST18 expression in epithelial cells is induced by tumor-associated macrophages, contributing to the reciprocal feed-forward loop between both cell types in liver tumorigenesis. Our findings warrant the exploration of means to interfere with ST18-dependent epithelium-macrophage interactions in a therapeutic setting. (Hepatology 2017;65:1708-1719).

Using combined evidence from replicates to evaluate ChIP-seq peaks.

MOTIVATION: Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) detects genome-wide DNA-protein interactions and chromatin modifications, returning enriched regions (ERs), usually associated with a significance score. Moderately significant interactions can correspond to true, weak interactions, or to false positives; replicates of a ChIP-seq experiment can provide co-localised evidence to decide between the two cases. We designed a general methodological framework to rigorously combine the evidence of ERs in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence. RESULTS: We applied our method to Myc transcription factor ChIP-seq datasets in K562 cells available in the ENCODE project. Using replicates, we could extend up to 3 times the ER number with respect to single-sample analysis with equivalent significance threshold. We validated the 'rescued' ERs by checking for the overlap with open chromatin regions and for the enrichment of the motif that Myc binds with strongest affinity; we compared our results with alternative methods (IDR and jMOSAiCS), obtaining more validated peaks than the former and less peaks than latter, but with a better validation. AVAILABILITY AND IMPLEMENTATION: An implementation of the proposed method and its source code under GPLv3 license are freely available at http://www.bioinformatics.deib.polimi.it/MSPC/ and http://mspc.codeplex.com/, respectively. CONTACT: marco.morelli@iit.it SUPPLEMENTARY INFORMATION: Supplementary Material are available at Bioinformatics online.

INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments.

MOTIVATION: Cellular mRNA levels originate from the combined action of multiple regulatory processes, which can be recapitulated by the rates of pre-mRNA synthesis, pre-mRNA processing and mRNA degradation. Recent experimental and computational advances set the basis to study these intertwined levels of regulation. Nevertheless, software for the comprehensive quantification of RNA dynamics is still lacking. RESULTS: INSPEcT is an R package for the integrative analysis of RNA- and 4sU-seq data to study the dynamics of transcriptional regulation. INSPEcT provides gene-level quantification of these rates, and a modeling framework to identify which of these regulatory processes are most likely to explain the observed mRNA and pre-mRNA concentrations. Software performance is tested on a synthetic dataset, instrumental to guide the choice of the modeling parameters and the experimental design. AVAILABILITY AND IMPLEMENTATION: INSPEcT is submitted to Bioconductor and is currently available as Supplementary Additional File S1. CONTACT: mattia.pelizzola@iit.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data.

BACKGROUND: Numerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets. RESULTS: To overcome the burden of integrating various data types with multiple tools, we developed two companion R/Bioconductor packages. The former, methylPipe, is tailored to the analysis of high- or low-resolution DNA methylomes in several species, accommodating (hydroxy-)methyl-cytosines in both CpG and non-CpG sequence context. The analysis of multiple whole-genome bisulfite sequencing experiments is supported, while maintaining the ability of integrating targeted genomic data. The latter, compEpiTools, seamlessly incorporates the results obtained with methylPipe and supports their integration with other epigenomics data. It provides a number of methods to score these data in regions of interest, leading to the identification of enhancers, lncRNAs, and RNAPII stalling/elongation dynamics. Moreover, it allows a fast and comprehensive annotation of the resulting genomic regions, and the association of the corresponding genes with non-redundant GeneOntology terms. Finally, the package includes a flexible method based on heatmaps for the integration of various data types, combining annotation tracks with continuous or categorical data tracks. CONCLUSIONS: methylPipe and compEpiTools provide a comprehensive Bioconductor-compliant solution for the integrative analysis of heterogeneous epigenomics data. These packages are instrumental in providing biologists with minimal R skills a complete toolkit facilitating the analysis of their own data, or in accelerating the analyses performed by more experienced bioinformaticians.

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data.

BACKGROUND: RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial. RESULTS: We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site's coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone. CONCLUSIONS: These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing.

Alterations in glutamate, arginine, and energy metabolism characterize cerebrospinal fluid and plasma metabolome of persons with HIV-associated dementia.

OBJECTIVES: HIV-associated dementia (HAD) is the most severe clinical expression of HIV-mediated neuropathology, and the processes underlying its development remain poorly understood. We aimed to exploit high-dimensional metabolic profiling to gain insights into the pathological mechanisms associated to HAD. DESIGN: In this cross-sectional study, we utilized metabolomics to profile matched cerebrospinal fluid (CSF) and plasma samples of HAD individuals ( n  = 20) compared with neurologically asymptomatic people with HIV (ASYM, n  = 20) and healthy controls (NEG, n  = 20). METHODS: Identification of plasma and CSF metabolites was performed by liquid-chromatography or gas-chromatography following a validated experimental pipeline. The resulting metabolic profiles were analyzed by machine-learning algorithms, and altered pathways were identified by comparison with KEGG pathway database. RESULTS: In CSF, HAD patients displayed an imbalance in glutamine/glutamate ratio, decreased levels of isocitrate and arginine, and increased oxidative stress when compared with ASYM or NEG. These changes were confirmed in matched plasma samples, which in addition revealed an accumulation of eicosanoids and unsaturated fatty acids in HAD individuals. Pathway analysis in both biological fluids suggested that alterations in several metabolic processes, including protein biosynthesis, glutamate and arginine metabolism, and energy metabolism, in association to a perturbed eicosanoid metabolism in plasma, may represent the metabolic signature associated to HAD. CONCLUSION: These findings show that HAD may be associated with metabolic modifications in CSF and plasma. These preliminary data may be useful to identify novel metabolic biomarkers and therapeutic targets in HIV-associated neurological impairment.

Fat-to-blood recirculation of partially dysfunctional PD-1+CD4 Tconv cells is associated with dysglycemia in human obesity.

Obesity is characterized by the accumulation of T cells in insulin-sensitive tissues, including the visceral adipose tissue (VAT), that can interfere with the insulin signaling pathway eventually leading to insulin resistance (IR) and type 2 diabetes. Here, we found that PD-1+CD4 conventional T (Tconv) cells, endowed with a transcriptomic and functional profile of partially dysfunctional cells, are diminished in VAT of obese patients with dysglycemia (OB-Dys), without a concomitant increase in apoptosis. These cells showed enhanced capacity to recirculate into the bloodstream and had a non-restricted TCRß repertoire divergent from that of normoglycemic obese and lean individuals. PD-1+CD4 Tconv were reduced in the circulation of OB-Dys, exhibited an altered migration potential, and were detected in the liver of patients with non-alcoholic steatohepatitis. The findings suggest a potential role for partially dysfunctional PD-1+CD4 Tconv cells as inter-organ mediators of IR in obese patients with dysglycemic.

Ultradeep sequencing analysis of population dynamics of virus escape mutants in RNAi-mediated resistant plants.

Plant artificial micro-RNAs (amiRs) have been engineered to target viral genomes and induce their degradation. However, the exceptional evolutionary plasticity of RNA viruses threatens the durability of the resistance conferred by these amiRs. It has recently been shown that viral populations not experiencing strong selective pressure from an antiviral amiR may already contain enough genetic variability in the target sequence to escape plant resistance in an almost deterministic manner. Furthermore, it has also been shown that viral populations exposed to subinhibitory concentrations of the antiviral amiR speed up this process. In this article, we have characterized the molecular evolutionary dynamics of an amiR target sequence in a viral genome under both conditions. The use of Illumina ultradeep sequencing has allowed us to identify virus sequence variants at frequencies as low as 2 × 10(-6) and to track their variation in time before and after the viral population was able of successfully infecting plants fully resistant to the ancestral virus. We found that every site in the amiR-target sequence of the viral genome presented variation and that the variant that eventually broke resistance was sampled among the many coexisting ones. In this system, viral evolution in fully susceptible plants results from an equilibrium between mutation and genetic drift, whereas evolution in partially resistant plants originates from more complex dynamics involving mutation, selection, and drift.

A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data.

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.